

import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_							:					int		 =   '''src/diffusers'''
UpperCAmelCase_							:					List[Any]		 =   '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
UpperCAmelCase_							:					str		 =   importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCAmelCase_							:					int		 =   spec.loader.load_module()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Union[str, Any]			,a__    :   str       )					->				Dict:
	return line.startswith(a__       ) or len(a__       ) <= 1 or re.search(r"""^\s*\)(\s*->.*:|:)\s*$"""			,a__       ) is not None
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple       )					->				Union[str, Any]:
	__A :			int		    =			object_name.split("""."""       )
	__A :			List[str]		    =			0

	# First let's find the module where our object lives.
	__A :			Any		    =			parts[i]
	while i < len(a__       ) and not os.path.isfile(os.path.join(a__			,f"""{module}.py"""       )       ):
		i += 1
		if i < len(a__       ):
			__A :			Any		    =			os.path.join(a__			,parts[i]       )
	if i >= len(a__       ):
		raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""       )

	with open(os.path.join(a__			,f"""{module}.py"""       )			,"""r"""			,encoding="""utf-8"""			,newline="""\n"""       ) as f:
		__A :			int		    =			f.readlines()

	# Now let's find the class / func in the code!
	__A :			int		    =			""""""
	__A :			Tuple		    =			0
	for name in parts[i + 1 :]:
		while (
		    line_index < len(a__       ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""			,lines[line_index]       ) is None
		):
			line_index += 1
		indent += "    "
		line_index += 1

	if line_index >= len(a__       ):
		raise ValueError(f""" {object_name} does not match any function or class in {module}."""       )

	# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
	__A :			Optional[int]		    =			line_index
	while line_index < len(a__       ) and _should_continue(lines[line_index]			,a__       ):
		line_index += 1
	# Clean up empty lines at the end (if any).
	while len(lines[line_index - 1]       ) <= 1:
		line_index -= 1

	__A :			Dict		    =			lines[start_index:line_index]
	return "".join(a__       )


UpperCAmelCase_							:					Tuple		 =   re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
UpperCAmelCase_							:					List[Any]		 =   re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
UpperCAmelCase_							:					Tuple		 =   re.compile(r'''<FILL\s+[^>]*>''')
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]       )					->				List[Any]:
	__A :			Optional[int]		    =			code.split("""\n"""       )
	__A :			Optional[int]		    =			0
	while idx < len(a__       ) and len(lines[idx]       ) == 0:
		idx += 1
	if idx < len(a__       ):
		return re.search(r"""^(\s*)\S"""			,lines[idx]       ).groups()[0]
	return ""
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[Any]       )					->				Dict:
	__A :			str		    =			len(get_indent(a__       )       ) > 0
	if has_indent:
		__A :			List[Any]		    =			f"""class Bla:\n{code}"""
	__A :			Optional[int]		    =			black.Mode(target_versions={black.TargetVersion.PYaa}			,line_length=119			,preview=a__       )
	__A :			int		    =			black.format_str(a__			,mode=a__       )
	__A    ,				__A :			Optional[int]		    =			style_docstrings_in_code(a__       )
	return result[len("""class Bla:\n"""       ) :] if has_indent else result
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str=False       )					->				Tuple:
	with open(a__			,"""r"""			,encoding="""utf-8"""			,newline="""\n"""       ) as f:
		__A :			int		    =			f.readlines()
	__A :			str		    =			[]
	__A :			Union[str, Any]		    =			0
	# Not a for loop cause `lines` is going to change (if `overwrite=True`).
	while line_index < len(a__       ):
		__A :			str		    =			_re_copy_warning.search(lines[line_index]       )
		if search is None:
			line_index += 1
			continue

		# There is some copied code here, let's retrieve the original.
		__A    ,				__A    ,				__A :			List[str]		    =			search.groups()
		__A :			Any		    =			find_code_in_diffusers(a__       )
		__A :			Dict		    =			get_indent(a__       )

		__A :			int		    =			line_index + 1 if indent == theoretical_indent else line_index + 2
		__A :			Optional[int]		    =			theoretical_indent
		__A :			List[Any]		    =			start_index

		# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
		__A :			Tuple		    =			True
		while line_index < len(a__       ) and should_continue:
			line_index += 1
			if line_index >= len(a__       ):
				break
			__A :			Optional[int]		    =			lines[line_index]
			__A :			Optional[int]		    =			_should_continue(a__			,a__       ) and re.search(f"""^{indent}# End copy"""			,a__       ) is None
		# Clean up empty lines at the end (if any).
		while len(lines[line_index - 1]       ) <= 1:
			line_index -= 1

		__A :			int		    =			lines[start_index:line_index]
		__A :			Optional[Any]		    =			"""""".join(a__       )

		# Remove any nested `Copied from` comments to avoid circular copies
		__A :			Dict		    =			[line for line in theoretical_code.split("""\n"""       ) if _re_copy_warning.search(a__       ) is None]
		__A :			Dict		    =			"""\n""".join(a__       )

		# Before comparing, use the `replace_pattern` on the original code.
		if len(a__       ) > 0:
			__A :			Tuple		    =			replace_pattern.replace("""with"""			,""""""       ).split(""","""       )
			__A :			Optional[int]		    =			[_re_replace_pattern.search(a__       ) for p in patterns]
			for pattern in patterns:
				if pattern is None:
					continue
				__A    ,				__A    ,				__A :			int		    =			pattern.groups()
				__A :			Optional[int]		    =			re.sub(a__			,a__			,a__       )
				if option.strip() == "all-casing":
					__A :			Union[str, Any]		    =			re.sub(obja.lower()			,obja.lower()			,a__       )
					__A :			Optional[Any]		    =			re.sub(obja.upper()			,obja.upper()			,a__       )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
			__A :			Union[str, Any]		    =			blackify(lines[start_index - 1] + theoretical_code       )
			__A :			int		    =			theoretical_code[len(lines[start_index - 1]       ) :]

		# Test for a diff and act accordingly.
		if observed_code != theoretical_code:
			diffs.append([object_name, start_index]       )
			if overwrite:
				__A :			str		    =			lines[:start_index] + [theoretical_code] + lines[line_index:]
				__A :			int		    =			start_index + 1

	if overwrite and len(a__       ) > 0:
		# Warn the user a file has been modified.
		print(f"""Detected changes, rewriting {filename}."""       )
		with open(a__			,"""w"""			,encoding="""utf-8"""			,newline="""\n"""       ) as f:
			f.writelines(a__       )
	return diffs
def 							__SCREAMING_SNAKE_CASE     (   a__    :   bool = False       )					->				Tuple:
	__A :			Any		    =			glob.glob(os.path.join(a__			,"""**/*.py"""       )			,recursive=a__       )
	__A :			Optional[Any]		    =			[]
	for filename in all_files:
		__A :			Dict		    =			is_copy_consistent(a__			,a__       )
		diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
	if not overwrite and len(a__       ) > 0:
		__A :			Any		    =			"""\n""".join(a__       )
		raise Exception(
		    """Found the following copy inconsistencies:\n"""
		    + diff
		    + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""       )


if __name__ == "__main__":
			UpperCAmelCase_							:					List[Any]		 =   argparse.ArgumentParser()
			parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
			UpperCAmelCase_							:					Optional[Any]		 =   parser.parse_args()

			check_copies(args.fix_and_overwrite)










import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import (
	    MraForMaskedLM,
	    MraForMultipleChoice,
	    MraForQuestionAnswering,
	    MraForSequenceClassification,
	    MraForTokenClassification,
	    MraModel,
	)
	from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=8,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=5,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=36,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=None,   )       ->   str:
						UpperCAmelCase_:		int							      =     parent
						UpperCAmelCase_:		Dict							      =     batch_size
						UpperCAmelCase_:		Optional[int]							      =     seq_length
						UpperCAmelCase_:		int							      =     is_training
						UpperCAmelCase_:		List[Any]							      =     use_input_mask
						UpperCAmelCase_:		int							      =     use_token_type_ids
						UpperCAmelCase_:		Tuple							      =     use_labels
						UpperCAmelCase_:		Tuple							      =     vocab_size
						UpperCAmelCase_:		int							      =     hidden_size
						UpperCAmelCase_:		List[str]							      =     num_hidden_layers
						UpperCAmelCase_:		List[str]							      =     num_attention_heads
						UpperCAmelCase_:		Any							      =     intermediate_size
						UpperCAmelCase_:		str							      =     hidden_act
						UpperCAmelCase_:		Optional[int]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		List[str]							      =     max_position_embeddings
						UpperCAmelCase_:		Optional[int]							      =     type_vocab_size
						UpperCAmelCase_:		Tuple							      =     type_sequence_label_size
						UpperCAmelCase_:		Tuple							      =     initializer_range
						UpperCAmelCase_:		str							      =     num_labels
						UpperCAmelCase_:		str							      =     num_choices
						UpperCAmelCase_:		Optional[int]							      =     scope

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		Union[str, Any]							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_token_type_ids:
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size					)

						UpperCAmelCase_:		str							      =     None
						UpperCAmelCase_:		List[Any]							      =     None
						UpperCAmelCase_:		Tuple							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size, self.seq_length],   self.num_labels					)
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.num_choices					)

						UpperCAmelCase_:		List[Any]							      =     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def      __snake_case       (self					)       ->   List[Any]:
						return MraConfig(
						    vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   is_decoder=SCREAMING_SNAKE_CASE_,   initializer_range=self.initializer_range,   )

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[Any]							      =     self.get_config()
						UpperCAmelCase_:		Dict							      =     300
						return config

			def      __snake_case       (self					)       ->   List[Any]:
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Any							      =     self.prepare_config_and_inputs()

						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Any							      =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
						UpperCAmelCase_:		List[str]							      =     ids_tensor([self.batch_size, self.seq_length],   vocab_size=2					)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[int]							      =     MraModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						UpperCAmelCase_:		Any							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     MraModel(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   encoder_attention_mask=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		int							      =     MraForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		int							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		str							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   start_positions=SCREAMING_SNAKE_CASE_,   end_positions=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)					)
						self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.num_labels
						UpperCAmelCase_:		Any							      =     MraForSequenceClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Tuple							      =     self.num_labels
						UpperCAmelCase_:		Any							      =     MraForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.num_choices
						UpperCAmelCase_:		int							      =     MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Dict							      =     input_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[str]							      =     token_type_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     input_mask.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		int							      =     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Any							      =     config_and_inputs
						UpperCAmelCase_:		Dict							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			A      		=	False
			A      		=	False
			A      		=	False
			A      		=	False

			A      		=	()

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Dict							      =     MraModelTester(self					)
						UpperCAmelCase_:		List[Any]							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   hidden_size=37					)

			def      __snake_case       (self					)       ->   Tuple:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_:		str							      =     type
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		List[str]							      =     MraModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)

			@unittest.skip(reason="""MRA does not output attentions"""					)
			def      __snake_case       (self					)       ->   List[str]:
						return







@require_torch
class 				_a				(   unittest.TestCase					):

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     MraModel.from_pretrained("""uw-madison/mra-base-512-4"""					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.arange(256					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Optional[int]							      =     torch.Size((1, 256, 768)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     torch.tensor(
						    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		List[str]							      =     MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""					)
						UpperCAmelCase_:		Dict							      =     torch.arange(256					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Any							      =     50265

						UpperCAmelCase_:		Any							      =     torch.Size((1, 256, vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     torch.tensor(
						    [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

			@slow
			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[int]							      =     MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""					)
						UpperCAmelCase_:		List[Any]							      =     torch.arange(4096					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Dict							      =     50265

						UpperCAmelCase_:		int							      =     torch.Size((1, 4096, vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     torch.tensor(
						    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)






